The increasing demand for underwater vehicles highlights the necessity for
robust localization solutions in inspection missions. In this work, we present
a novel real-time sonar-based underwater global positioning algorithm for AUVs
(Autonomous Underwater Vehicles) designed for environments with a sparse
distribution of human-made assets. Our approach exploits two synergistic data
interpretation frontends applied to the same stream of sonar data acquired by a
multibeam Forward-Looking Sonar (FSD). These observations are fused within a
Particle Filter (PF) either to weigh more particles that belong to
high-likelihood regions or to solve symmetric ambiguities. Preliminary
experiments carried out on a simulated environment resembling a real underwater
plant provided promising results. This work represents a starting point towards
future developments of the method and consequent exhaustive evaluations also in
real-world scenarios.

在此研究中，我们提出了一种针对海底人造资产稀疏环境设计的自主水下航行器的新型实时声纳全球定位算法。通过利用两种数据解释前端，并在同一声纳数据流中合并这些观测结果，我们的方法在粒子滤波器内加权高概率区域的粒子或解决对称模糊。在模拟环境中进行了初步实验，结果显示出良好的前景。这项工作为将来的方法发展和在实际情况下的全面评估奠定了基础。

基于声纳的水下 AUV 定位系统在低基础设施密度环境中的应用

A Sonar-based AUV Positioning System for Underwater Environments with  Low Infrastructure Density

Accurate metrical localization is one of the central challenges in mobile
robotics. Many existing methods aim at localizing after building a map with the
robot. In this paper, we present a novel approach that instead uses geotagged
panoramas from the Google Street View as a source of global positioning. We
model the problem of localization as a non-linear least squares estimation in
two phases. The first estimates the 3D position of tracked feature points from
short monocular camera sequences. The second computes the rigid body
transformation between the Street View panoramas and the estimated points. The
only input of this approach is a stream of monocular camera images and odometry
estimates. We quantified the accuracy of the method by running the approach on
a robotic platform in a parking lot by using visual fiducials as ground truth.
Additionally, we applied the approach in the context of personal localization
in a real urban scenario by using data from a Google Tango tablet.

本文提出了一种使用 Google Street View 中地理标记全景作为全局定位源的新方法，通过追踪短单眼相机序列中的特征点，估计 3D 位置，然后计算 Street View 全景图与估计点之间的刚体变换，进而实现了机器人自动测距。